The internet is full of interesting information. Being able to collect that data for different purposes would be awesome, right? Thanks to web scraping, that's possible.
In this guide, you'll dig into what web scraping is, when to use it, and the best ways to extract the information. Let's dive in!
What Is Web Scraping?
Web scraping is the process of extracting online public data, usually through specialized software.
Trillions of gigabytes of information are generated and published online every day. Since data is nowadays far more valuable than oil, that represents a huge opportunity. To gain a competitive advantage, companies need a way to retrieve it and use it for their strategic goals.
In the early days of the internet, collecting data was time-consuming and labor-intensive. Over time, data scraping technologies have become more sophisticated. Today, the process is automatic and much easier, thanks to the many tools available.
What Is Web Scraping Used For?
Web scraping is a versatile technique with widespread applications. Its flexibility allows it to adapt to different sectors and scenarios. Let's explore the most common web scraping use cases: market research, lead generation, price monitoring, sentiment analysis, content aggregation, weather data collection, Search Engine Optimization (SEO), logistics and supply chain, healthcare, and machine learning and AI.
Market Research
- Real estate: Performing supply/demand analysis, identifying market opportunities and trending areas, tracking price fluctuation, etc.
- E-commerce: Getting product details, prices, customer reviews, etc.
- Automotive: Tracking dealer's distribution, most popular models, best deals, supply by city, etc.
- Travel and accommodation: Extracting data on available rooms, hottest areas, best discounts, prices by season, and more.
- Job postings: Identifying the most in-demand jobs, rising industries, best-paying employers, etc.
- Social media: Establishing a brand presence, tracking growing influencers, discovering new acquisition channels, and targeting audiences.
- City discovery: Detecting trending areas, new restaurants, commercial streets, and shops.
Lead Generation
- Marketing and sales: Extracting contact information from social media public profiles and data on potential customers for targeted campaigns.
- Recruitment: Gathering resumes and candidate details from job portals.
Price Monitoring
- Retail: Keeping track of prices on different e-commerce platforms.
- Stocks and finance: Extracting data on stock prices, news, financial reports, volume activity, anomalies, etc.
- Travel: Tracking flight fares, hotel rates, and prices of vacation packages.
- Energy and market: Scraping prices of oil, gas, electricity, and commodities.
Sentiment Analysis
- Social media: Analyzing public opinion through tweets, posts, comments, and trending hashtags.
- Customer satisfaction: Monitoring reviews, opinions, and feedback from various platforms.
Content Aggregation
- Media and journalism: Collecting headlines, articles, and news updates from many sources.
- Research: Retrieving papers and publications from academic databases.
- Government: Scraping official statements, press releases, and public records.
- Comparisons: Extracting data, information, statistics, and reviews on comparable products and services.
- Education: Collecting data and resources for academic projects and learning materials.
Weather Data Collection
- Agriculture: Collecting weather forecasts, rainfall patterns, and temperature data.
- Transportation: Monitoring weather conditions for route planning and safety.
- Renewable energy: Gathering wind speed, solar radiation, and climate data for energy generation.
Search Engine Optimization (SEO)
- Digital Marketing: Tracking keywords' relevance, search rankings, and backlinks.
- Blogging: Monitoring content metrics and performance data to create SEO-driven content strategies.
Logistics and Supply Chain
- Supplier data: Scraping supplier catalogs, pricing, and product information for procurement purposes.
- Shipping and tracking: Tracking shipment tracking details, delivery statuses, and logistics data.
- Demand forecasting: Gathering market data, pricing trends, and inventory levels for demand planning and optimization.
Healthcare
- Research: Extracting medical research papers, clinical trial data, and healthcare provider information.
- Patient Feedback: Gathering patient reviews, opinions, and feedback on treatments and drugs.
Machine Learning and AI
- Model training: Scraping data for training machine learning models.
- Image recognition: Collecting images with labeled metadata for computer vision projects.
- Natural language processing: Collecting text for language analysis and text generation through AI.
Is Web Scraping Legal?
Web scraping is legal, but it's essential to follow the guidelines specified in the Terms of Service of the target site. Also, some websites may contain personal or sensitive information behind a login wall. You need proper consent to collect that data. You should avoid retrieving private or confidential information. Also, be sure you aren't violating copyright or intellectual property rights.
In short, web scraping is legal and ethical if you respect the boundaries of the Terms of Service and don't violate copyright and privacy.
Types of Web Scrapers
Web scrapers differ a lot depending on their goals and target audience, but they can be boiled down to four types:
- Custom-built scrapers: Programs built by developers and based on several technologies. They are the most flexible and large-scale solution.
- Browser extension scrapers: Add-ons for the most popular browsers, such as Chrome, Firefox, and Safari. They make it possible to select and extract data from sites while browsing. Users can employ their intuitive interface to extract data through point-and-click interactions. They're an effective solution for quick tasks but are typically pretty limited.
- Desktop scrapers: Standalone applications installed on a computer to automate data scraping processes. In most cases, these are no-code tools that make it easier to extract data from sites via a point-and-click interface. They often provide features for beginners and advanced users, including task scheduling, data parsing, and export options.
- Cloud-based scrapers: Scalable and distributed scraping solutions that operate in the cloud. They're a great tool to handle large-scale data extraction tasks. Generally, they offer data processing and storage capabilities. You can access them through remote commands to schedule tasks. The possibilities (websites and contents) are more limited than with custom-built scrapers.
Each type of scraper has its own advantages and use cases. To choose the right one, consider the task complexity, the data volume to collect, and the project's scalability and technical requirements.
What Is the Process of Web Scraping
The manual approach to web data scraping takes only two steps:
- Open pages in the browser.
- Copy and paste information from them.
The main problem with this solution is that it isn't scalable, as it requires a lot of time and manpower. That's why web scraping usually refers to an automated process, most typically performed by developers. A program communicates with websites and gets the desired data for you.
The client (you) sends a request to a server (the website), which responds with an HTML document. This content gets then selectively extracted (e.g., product names and prices). Finally, you export the data in a convenient format, like Excel or CSV.
Thus, a web scraping process implements the four main tasks outlined below:
- Inspect the target site: Launch your browser and spend some time on the site you want to extract data from. Get familiar with its structure and understand what information you can recover. Find out on what pages the most important data is and its format.
- Download the HTML document: Perform an HTTP request to retrieve the HTML document associated with a target page. To do so, you'll need an HTTP client library.
- Extract your data from the HTML document: Select the information you want to and extract it. This step requires an HTML parser.
- Export the scraped data: Once obtained, transform and store it in a format that makes it easier to use, such as CSV or JSON. You can also store it in a database.
A web scraper is a software program that executes these operations to get data from websites. Building one comes with some challenges. Fortunately, you can overcome them with the right web scraping tools.
If you're a developer, ZenRows will become your best friend. It reduces web scraping to API calls. On top of that, it can also protect your IP with proxies and bypass any anti-scraping solutions for you. What a game-changer!
Data Scraping: The Techniques You Need to Know
To get a better grasp of how web scraping works and the decisions and challenges you'll face, let's explore the most fundamental ones.
Getting Started
You may not even need web scraping to get data from the web, as some sites expose their data through APIs. These provide an easy way to access specific content. At the same time, you depend on what the provider offers and a possible external decision to limit the access or charge for it. In other words, you aren't in control.
Instead, data scraping gives you access to all publicly available data, even if not available via API. It takes longer to develop and maintain, but scraping is the best solution in most cases. To learn more, check out our article on web scraping vs. API.
Fundamentals
Choosing the right programming language is a critical step for data scraping. Python, JavaScript, or Ruby offer popular libraries for scraping data, but they may not be the most efficient.
To pick the right technology, you need to reflect on the requirements of your project. Consider factors like performance, ease of use, community support, and available tools. Our guide on the best programming languages for web scraping might be helpful for you.
Keep in mind that you usually don't know all the URLs in advance (e.g., think of a search on Amazon with many result pages). Here's where web crawling comes to the rescue! This process involves navigating through a website to discover and index new pages, and it's generally part of the scraping logic. Take a look at our web crawling vs. web scraping guide to understand the differences between the two.
Challenges
Data scraping comes with tons of challenges, and the most basic lies in website updates. Whenever the HTML content of a page changes, you need to adjust your scraper accordingly. To address that, monitor your scrapers to spot errors and be ready to fix them. Maintenance is the key.
Dynamic content and JavaScript-rendered pages can be another issue. Traditional scraping techniques fail when getting data from those sites, so you'll need a headless browser like Selenium.
However, the biggest challenge is to avoid getting blocked. Popular sites adopt powerful technologies to protect themselves from bots and scrapers. These include IP banning, CAPTCHAs, firewalls, and many more.
There are several ways to bypass these measures, and you may need to combine some of them. Take a look at our tutorial to become a master in the art of web scraping without getting blocked.
Conclusion
This in-depth article explained what website scraping is about. Now, you know:
- What scraping is.
- When to use it and what benefits it can bring.
- Whether it's legal.
- The most important fundamentals related to data scraping.
The other side of the coin is anti-scraping. That includes technologies that can block your scrapers anytime. Get around them with ZenRows, a web scraping API with the most effective anti-bot bypass capabilities on the market. Get the desired data from any site with a single API call.
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